Method library WORKFLOW / 036

Research workflow/4 steps

Synthetic control, the tidy way — weights, gaps and placebo inference (tidysynth)

@tidysynth ✓ P D · Jun 29, 2026 · 1.2k views

@misc{tidysynth,
  title        = {tidysynth},
  author       = {Eric Dunford},
  howpublished = {\url{https://github.com/edunford/tidysynth}},
  note         = {Software / documentation}
}

In the margins · Peer notes@diego_morales · Showing the donor and predictor weights side by side is underrated — it's the honest answer to 'where does the cou… 2

The method workspace

Follow the analysis.

Select a node to explore the method

Method mapInput → 4 steps → output

START WITHInput data

ARRIVE ATResults

Read step 01

Inside the methodReasoning, equations & implementation

Data prep

Panel, treated unit, donor pool

Step notesField note & discussion1

Data preparation — shapes the raw inputs into what the estimator expects.

What happens here

Pick the treated unit and intervention time; the rest of the units form the donor pool. Identification rests on a good pre-treatment fit.

Formula

\hat Y^{N}_{1t}=\textstyle\sum_{j\ge 2}\hat w_j\,Y_{jt}

Reads fromNot specified Feeds intoNot specified

Key code

synthetic_control(outcome = cigsale, unit = state,
                  time = year, i_unit = 'California', i_time = 1988)

Reference / docs ↗

Discussion on this step (0)

StatsOtterDiscussion starter · Editorial note

Before using this step, make its assumptions and its output explicit. A useful peer note explains what you would check, what could go wrong, or what would change your interpretation.

Questions to explore

Which assumption matters most here, and what evidence could challenge it?
What would you check before passing this output to the next step?

Peer discussion

Bring your experience, a question, or a different perspective.

Estimation

Solve for donor & predictor weights

Step notesField note & discussion1

The core estimate — where the causal quantity itself is computed.

What happens here

Convex weights make the synthetic unit match the treated unit's pre-period predictors as closely as possible.

Formula

\hat W=\arg\min_{W}\ \lVert X_1-X_0W\rVert_V\ \ \text{s.t.}\ w_j\ge 0,\ \textstyle\sum_j w_j=1

Reads fromNot specified Feeds intoNot specified

Key code

generate_weights() %>% generate_control()

Reference / docs ↗

Discussion on this step (0)

StatsOtterDiscussion starter · Editorial note

Before using this step, make its assumptions and its output explicit. A useful peer note explains what you would check, what could go wrong, or what would change your interpretation.

Questions to explore

Which assumption matters most here, and what evidence could challenge it?
What would you check before passing this output to the next step?

Peer discussion

Bring your experience, a question, or a different perspective.

Reporting

Read the gap: observed − synthetic

Step notesField note & discussion1

Reporting — turn the numbers into a figure or table a reader can act on.

What happens here

The post-treatment gap between the treated unit and its synthetic twin is the estimated effect over time.

Formula

\hat\tau_t=Y_{1t}-\hat Y^{N}_{1t}

Reads fromNot specified Feeds intoNot specified

Key code

plot_trends(out); plot_differences(out)

Reference / docs ↗

Discussion on this step (0)

StatsOtterDiscussion starter · Editorial note

Before using this step, make its assumptions and its output explicit. A useful peer note explains what you would check, what could go wrong, or what would change your interpretation.

Questions to explore

Which assumption matters most here, and what evidence could challenge it?
What would you check before passing this output to the next step?

Peer discussion

Bring your experience, a question, or a different perspective.

Inference

Placebo permutation across donors

Step notesField note & discussion1

Uncertainty quantification — standard errors, intervals, and aggregation.

What happens here

Re-run the whole procedure pretending each donor was treated; rank the real gap against that placebo distribution via the RMSPE ratio.

Formula

r_1=\dfrac{\mathrm{RMSPE}_{\text{post}}}{\mathrm{RMSPE}_{\text{pre}}},\qquad p=\tfrac{\#\{j: r_j\ge r_1\}}{J+1}

Reads fromNot specified Feeds intoNot specified

Key code

plot_placebos(out); plot_mspe_ratio(out)

Reference / docs ↗

Discussion on this step (0)

StatsOtterDiscussion starter · Editorial note

Before using this step, make its assumptions and its output explicit. A useful peer note explains what you would check, what could go wrong, or what would change your interpretation.

Questions to explore

Which assumption matters most here, and what evidence could challenge it?
What would you check before passing this output to the next step?

Peer discussion

Bring your experience, a question, or a different perspective.

Data contract

Inputs & data

A balanced panel (unit × time) with one treated unit switching on at a known time, a donor pool, and a few pre-treatment predictors.

Show data format & exampleHide example

Format — long panel: unit, time, outcome, predictors; one treated unit after T0.

library(tidysynth)
out <- panel %>%
  synthetic_control(outcome = cigsale, unit = state, time = year,
                    i_unit = 'California', i_time = 1988) %>%
  generate_predictor(...) %>% generate_weights() %>% generate_control()

Read the evidence

Results & interpretation

2 figures Workflow discussion 2

01FigurePlacebo gaps (Abadie's California Prop-99): the treated unit's gap (magenta) plunges past nearly every donor-pool placebo after the 1988 intervention.

02FigureHow the synthetic control is built: convex weights on donor states (Utah, Nevada, …) and on the predictor variables.

Figures reproduced from tidysynth — Eric Dunford — unofficial community showcase; all credit to the original authors.

Context & interpretation

⚠️ Unofficial community showcase of tidysynth. Not affiliated with the authors; all credit to them.

Build a synthetic version of the treated unit from a convex blend of donors, read the treated-minus-synthetic gap, and test it against placebos run on every donor.

Peer notes

Discuss the workflow (2)

2

@synthetica · Jun 29, 2026

The pipe-friendly API finally makes SC readable end to end. plot_placebos() as the inference step is the part people skip.
0

@diego_morales · Jun 29, 2026

Showing the donor and predictor weights side by side is underrated — it's the honest answer to 'where does the counterfactual come from?'.

1

@panel_panda · Jun 29, 2026

Agreed. If one donor carries 0.8 of the weight, the 'synthetic' control is basically that one unit and you should say so.